A List of Things

Teaching

Academic Teaching

Semester 1, Academic Year 2022/2023:

  • DSA3101 Data Science in Practice
  • DSA2101 Essential Data Analytics Tools: Visualisation

Both of these courses are vital training for data scientists, so, as usual, we shall be utilising the excellent resources from .  For the duration of the semester, students will have access to the videos, R tutorials and exercises on that website. Datacamp’s learn-by-doing methodology will tie in well with the activities that we have in class. More information will be provided once class begins!

Semester 2, Academic Year 2021/2022:

  • DSA3101 Data Science in Practice
  • DSA3310/DSA3311 Industry Internship Attachment

Both of these courses are vital training for data scientists, so, as usual, we shall be utilising the excellent resources from .  For the duration of the semester, students will have access to the videos, R tutorials and exercises on that website. Datacamp’s learn-by-doing methodology will tie in well with the activities that we have in class. More information will be provided once class begins!

Semester 1, Academic Year 2021/2022:

  • DSA2101 Essential Data Analytics Tools: Visualisation

As usual, we shall be utilising the excellent resources from .  For the duration of the semester, students will have access to the videos, R tutorials and exercises on that website. Datacamp’s learn-by-doing methodology will tie in well with the activities that we have in class. More information will be provided once class begins!

Special Semester, Academic Year 2020/2021:

  • DSA3310/DSA3311 Industry Internship Attachment

This course is critical for data science majors to gain practical experience.  The excellent resources at    can provide videos, tutorials and exercises on new topics that you might need to quickly learn and apply on your projects.

Semester 2, Academic Year 2020/2021:

  • DSA2101 Essential Data Analytics Tools: Visualisation

As usual, we shall be utilising the excellent resources from .  For the duration of the semester, students will have access to the videos, R tutorials and exercises on that website. Datacamp’s learn-by-doing methodology will tie in well with the activities that we have in class. More information will be provided once class begins!

Semester 1, Academic Year 2020/2021:

  • DSA2101 Essential Data Analytics Tools: Visualisation
  • IND5003 Data Analytics for Sense-making

For both of these classes, we shall be utilising the excellent resources from .  For the duration of the semester, students will have access to the videos, R tutorials and exercises on that website. Datacamp’s learn-by-doing methodology will tie in well with the activities that we have in class. More information will be provided once class begins!

Special Term, Academic Year 2019/2020:

  • DSA1361: Introductory Data Science with Python and Tableau

For this class, we shall be utilising the excellent resources from .  For the duration of the semester, students will have access to the videos, R tutorials and exercises on that website. Datacamp’s learn-by-doing methodology will tie in well with the activities that we have in class. More information will be provided once class begins! We shall also be using Tableau Desktop and Tableau Online for this class.

Semester 1, Academic Year 2019/2020:

  • DSA2101 Essential Data Analytics Tools: Visualisation
  • IND5003 Data Analytics for Sense-making

Special Term, Academic Year 2018/2019:

  • DSA1361: Introductory Data Science with Python and Tableau

Semester 2, Academic Year 2018/2019:

  • ST2131/MA2216 Probability

Semester 1, Academic Year 2018/2019:

  • DSA2101 Essential Data Analytics Tools: Visualisation

Special Term, Academic Year 2017/2018:

  • DSA1361: Introductory Data Science with Python and Tableau

Semester 2, Academic Year 2017/2018:

  • ST2131/MA2216 Probability

Semester 1, Academic Year 2017/2018:

  • ST3233 Applied Time Series Analysis
  • DSA2101 Essential Data Analytics Tools: Data Visualisation

Semester 2, Academic Year 2016/2017:

  • ST2131/MA2216 Probability

Semester 1, Academic Year 2016/2017:

  • ST5226 Spatial Statistics

Semester 2, Academic Year 2015/2016:

  • ST2131/MA2216 Probability
  • ST3247 Simulation

Semester 1, Academic Year 2015/2016:

  • ST1232 Statistics for Life Sciences.
  • ST3233 Applied Time Series Analysis

Semester 2, Academic Year 2014/2015:

  • ST1232 Statistics for Life Sciences.
  • ST3247 Simulation

Semester 1, Academic Year 2014/2015:

  • ST1232 Statistics for Life Sciences

Semester 2, Academic Year 2013/2014:

  • ST3247 Simulation

Non-academic Teaching/Talks

If you are interested in other courses (on R or otherwise), do email me and I shall see if one can be customised for your needs.

Year 2020:

  1. Mar 2020: Together with my colleague David Chew , I conducted a workshop for Junior College math teachers to develop activities that they can use in class to introduce the ideas in data science and analysis. The activity revolved around using simple correlation to develop a face ID system. At the end, of course, we demonstrated how well AWS does it.
  2. Aug 2020: I gave a department seminar on an R package that could be useful for instructors who wish to auto-grade R assignments. Here is the material from that talk.

Year 2019:

  1. Jan 2019: The Lee Kuan Yew School of Public Policy conducted a 1-week workshop on Understanding and Communicating Risks. This year, Pablo Suarez facilitated the excellent Decisions for the Decade game, while Yap Von Bing and I provided the probabilistic background for it. Here are the notes I used.
  2. Jun 2019: Statistics Enrichment Camp. Participants used Arduinos to collect their heart-rates in a few different scenarios. The activity followed this worksheet. The aim was to highlight what goes on in a data analysis project, from the hardware and software tools, through the cleaning, and finally up to the analysis. After the data collection, we attempted to analyse the data on the spot. That didn’t work out as hoped, but here is a web-page with a summary of the data that we did manage to collect.
  3. Oct 2019: As part of research data day, I gave a short talk on tidy data. These are the slides.
  4. Nov 2019: I was involved in the NUS festival of Learning, where I gave a talk on an R package that we are developing. Here are the slides.

Year 2018:

  1. Jan 2018: The Lee Kuan Yew School of Public Policy conducted a 1-week workshop on Understanding and Communicating Risks. I conducted a couple of sessions, on “Pitfalls in Data Analysis” and “Risk in Complex Systems”. The notes that I used can be found here.
  2. May 2018: For the Open House talk this year, I spoke about the numerous data collection tools available today. Together with the help of a dedicated student, I used Arduinos, Raspberry Pi‘s and the Google assistant SDK to demonstrate an Internet-of-Things setup. These are the handouts that I used.
  3. Sep 2018: I conducted a half-day workshop to high-school teachers who wished to know more about data science, and how it is taught here at our department. We ran through a few data collection activities and then took a quick look at the data on Tableau. One of the activities was this Scratch game that I hacked together from the Raspberry PI site. It is a real keyboard smasher!
  4. Nov 2018: I attended the Hikone Data Science conference on education in data science. It was a small workshop; I learnt a great deal from the speakers there. My talk was on “Teaching Visualisation in Data Science”.

Year 2017:

  1. Jan 2017: A 1-day workshop on exploratory statistics and graphics for the United Nations in Bangkok UNESCAP. The slides can be accessed here.
  2. May 2017: These are the slides for the Faculty of Science Open House on May 13th 2017. The talk was entitled Statistics and the NBA.
  3. June 2017: Statistics Enrichment Camp. We used an Arduino-based lie-detector and a simulated crime scenario to discuss the job of a statistician. Here is a web-page with more information on the activity.

May – August 2014:

  1. A half-day workshop at the 2nd International Conference on Big Data and Analytics in Healthcare, BDAH 2014. The workshop is entitled Case Study Based Introduction to Healthcare Analytics with R. All material can be found on github at the singator/bdah repository.
  2. Introduction to R, A 2-day course for an internal department within NUS.

Awards

  1. Apr 2022: NUS Annual Teaching Excellence Award. (Honour Roll).
  2. Dec 2021: Faculty Teaching Honour Roll.
  3. Apr 2021: NUS Annual Teaching Excellence Award.
  4. Dec 2020: Faculty Teaching Excellence Award. The courses that I taught in consideration of this award were
    • DSA2101 Essential Data Analytics Tools: Visualisation
    • IND5003 Data Analytics for Sense-making
    • DSA1361: Introductory Data Science with Python and Tableau
  5. Dec 2019: NUS Annual Teaching Excellence Award.
  6. Dec 2019: Faculty Teaching Excellence Award. The courses that I taught in consideration of this award were
    • DSA2101 Essential Data Analytics Tools: Visualisation
    • IND5003 Data Analytics for Sense-making
    • ST2131/MA2216 Probability
  7. Dec 2018: Faculty Teaching Excellence Award. The courses that I taught in consideration of this award were
    • ST3233 Applied Time Series Analysis
    • DSA2101 Essential Data Analytics Tools: Visualisation
    • ST2131/MA2216 Probability
  8. Dec 2016: Faculty Teaching Excellence Award. The courses that I taught in consideration of this award were
    • ST1232 Statistics for the Life Sciences
    • ST3233 Applied Time Series Analysis
    • ST2131/MA2216 Probability
    • ST3247 Simulation
  9. Dec 2014: Faculty Teaching Excellence Award. The course that I taught in consideration of this award was
    • ST3247 Simulation

UROPS

  1. In AY18/19 Sem I, we worked on analysing a dataset from a gamification experiment conducted by a colleague from the department of Anatomy. The outcome was a paper submitted to a journal. A longer version of it is on arXiv.
  2. In AY17/18, we used a Raspberry PI to capture images of a parking lot, and then applied a CNN to classify whether a lot was occupied or not. The collection, cleaning and preparation of the dataset was a huge task, so we are sharing it online, along with the TensorFlow coded that my student wrote. It can be accessed here.

Research

Recent submissions:

  • WNAR(2022) I gave a talk at WNAR 2022, a virtual conference, as part of a session with Claudio, James and Njesa on “Emerging Challenges and Opportunities in Statistics for Higher-Education and Organizations“. These are the slides I used.
  • DirtyDF (2021) This is a python module for “dirtying” datasets. This can be useful for generating different versions of a single dataset for students to practice with. More information can be found here.
  • Semi-automatic Grader for R/Rmd Scripts (2021), useR 2021. Here are the slides and manuals, and here is the package. The full talk/session can be watched here.
  • Methodology for future flood assessment in terms of economic damage: development and application for a case study in Nepal (2020), Marie Delalay, Alan D Ziegler, Mandira Shrestha, Vik Gopal (Journal of Flood Risk Management, here is a link to it).
  • MEchanical DIlatation of the Cervix­­ in a Scarred uterus (MEDICS): the study protocol of a randomised controlled trial comparing a single cervical catheter balloon and prostaglandin PGE2 for cervical ripening and labour induction following caesarean deliver (2019), Soe-Na Choo, Abhiram Kanneganti, Muhammad Nur Dinie Bin Abdul Aziz, Leta Loh, Carol Hargreaves, Vikneswaran Gopal, Arijit Biswas, Yiong Huak Chan, Ida Suzani Ismail, Claudia Chi, Citra Mattar (link to paper in BMJ Open).
  • Land-use and land-cover classification using Sentinel-2 data and machine-learning algorithms: operational method and its implementation for a mountainous area of Nepal (2019), Marie Delalay, Varun Tiwari, Alan D. Ziegler, Vik Gopal and Paul Passy (link to paper in Journal of Applied Remote Sensing)
  • Assessing the Impact of Gamification on Self-Directed Learning in First-Year Medical Students (2018), Lee De Zhang, Vik Gopal, Chan Jia Min, Ng Li Shia and Ang Eng Tat (submitted for consideration, but a longer version is available here at arXiv)
  • Gamifying Anatomy Education (2018), Ang Eng Tat, Chan Jia Min, Vik Gopal and Ng Li Shia (access it here in Clinical Anatomy)
  • Importance of miRNA stability and alternative primary miRNA isoforms in gene regulation during Drosophila development (2018), Li Zhou, Mandy Yu Theng Lim, Prameet Kaur, Abil Saj, Diane Bortolamiol-Becet, Vik Gopal, Nicholas Tolwinski, Greg Tucker-Kellogg and Katsutomo Okamura (access it here in eLife)
  • A Constrained Conditional Likelihood Approach for Estimating the Means of Selected Populations (2017), Claudio Fuentes, Vik Gopal. (Link to arXiv paper)
  • A Spatio-Temporal Modeling Approach for Weather Radar Reflectivity Data and Its Applications in Tropical Southeast Asia (2016), Liu Xiao, Vik Gopal, Jayant Kalagnanam. (Appears in Annals of Applied Statistics. Access it here.)
  • popKorn: An R package for the interval estimation of the mean of the selected populations (2013), Vik Gopal, Claudio Fuentes, useR! 2014.
  • Statistical forecasting of rainfall from radar reflectivity in Singapore (2014), Xiao Liu, Vik Gopal, Lloyd Treinish, 94th AMS meeting.
  • kPop: An R package for the interval estimation of the mean of the selected populations (2013), Vik Gopal, Claudio Fuentes, useR! 2013.
  • Prediction of duration and impact of non-recurrent events in transportation networks (2013), Sebastien Blandin, Vik Gopal, Karthik Thirumalai and Joanne Cheong, TRISTAN VIII.

Toolbox

Here is a small collection of tools that I have found that I cannot do without in my everyday work:

  • R. This really goes without saying. It’s like a cleaver to a butcher. More recently (2016) I have become quite dependent on RStudio, an (the?) IDE for R.
  • Sweave and more recently, markdown. These allow one to intersperse code, code output (including plots) into beautifully typeset pdf or html documents. This entire page was created using R markdown, in fact.
  • git. Once you get used to this source-code management tool, you will be angry that no one introduced you to it earlier. Coupled with bitbucket or github and you can have ALL your documents/write-ups and code synchronised.
  • vim. There is now a decent integration with R, via RStudio. The truth is, however, I have found this to be such a superb tool for text editing that I just cannot do without it. Once you get used to working with split windows, netrw, recording, and the tremendous search-and-replace commands you will never use another editor.

Contact

Email: vik DOT gopal AT nus DOT edu DOT sg (preferred method of contact)

Office: S16 04-06 (please do email first to make sure I’m there)